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Biology subjects

Eriksson, T.

Publications and source records attributed to Eriksson, T..

3 recordsLinked to original sources

Quantifying electro-osmotic flow in mutant MspA nanopores

Nanopores have emerged as a powerful tool for single-molecule sensing and sequencing. In a typical nanopore experiment, a charged analyte such as DNA is driven through the pore by an electrophoretic force generated by the applied electric field. This can be problematic, however, when analytes such as proteins carry mixed charges. An alternative driving force can be from electro-osmotic flow (EOF), the water flow that arises from an asymmetric migration of cations and anions through the nanopore. However, despite much recent interest, there is no straightforward experimental method to quantify EOF in nanopores. Here, we trap a dipolar peptide in an MspA nanopore, where studying its escape rate versus voltage allows us to extract the work done by the EOF in various MspA mutants and wildtype MspA compared to a M2-MspA reference. For the 30 amino acid model peptide, this yields values of ~5.6-16.7 kBT for the work done by the EOF at 80 mV. Finite-element simulations confirm the trends observed in the experiments. Charged mutations close to the nanopore's constriction site are found to produce the highest EOF. Our novel method provides a quantitative metric of the EOF in nanopores, opening up its study and broad use in future nanopore sequencing and sensing applications.

biophysics↗

Genetic associations of dimensional autistic phenotypes

Since there is a large variation in the symptoms shown by persons affected with ASD, analyzing genetics data using a case-control design is not straightforward. To avoid the difficult problem of defining heterogeneous groups, we used four different methods to compute a latent representation of a merged set of three psychometric tests. Computing the genetic contribution of each representation using a subset of participants with genetic data, we showed that factor analysis as well as variable autoencoders separates information contained in psychometric tests into genetically distinct phenotypic domains. Using the individual-level loadings of the domains as quantitative phenotypes in genome-wide association studies we detected statistically significant genetic associations in the domain related to insistence on routine, as well as suggestive genetic signals in other domains. We hope that these results can suggest possible domain-specific interventions in the future.

genetics↗

Predicting protease networks through human genetics

By utilizing functional genetic variation within the participants of the UK Biobank project for a largescale PheWAS study we attempted to get a better understanding of how the set of human proteases and their endogenous inhibitors are involved in common diseases. Focusing on known human proteases, their inhibitors, and known substrates, we computed their ranked-biased similarity from phenome-wide association results. Putative regulatory networks were constructed from 250 high-scoring pairs of proteases and related genes. This analysis suggested thirteen network modules, five diagnosis-based and eight biomarker-based. Through genetic associations and published literature on module members, the modules could be classified into different disease modalities including cholesterol homeostasis and high blood pressure.

bioinformatics↗